From pattern to function: eco-evolutionary representations of complex spatial structure for the new era of spatial biology

通过成像技术和大规模处理这些图像的能力的创新， 分辨率成像正在改变分子生物学的领域，但它的力量尚未得到充分利用 提出进化生物学的问题。就像人口统计调查可以或多或少地揭示 例如，在人口稠密的地区，传染病可能以不同的速度传播，这些成像 数据集可以帮助我们量化空间变异的细胞和分子模式，并了解这是如何发生的。 变异通过阻碍或加速新变异的传播来影响进化的速度。 人口我的研究项目，在计算机视觉和进化生物学的界面上，正在探索 分子和细胞群落如何在空间上组织，以及由此产生的空间拓扑如何 产生、稳定维持并进一步塑造进化过程的结果。 什么样的空间拓扑结构可以放大新突变的选择优势？ 相对于抑制选择力和减缓进化速度的结构，我们建立 理论进化模型，探索进化的速度是如何形成的复杂的空间结构， 真和ND进化扩增或选择性抑制的相关空间特征。我们链接 这些理论人口遗传模型，以高分辨率成像数据集，并研究由此产生的 空间架构这使我们能够超越简单地描述细胞或分子的空间模式， 变异，并使探索的生成过程，以及进化的轨迹， the system. 超越对这些问题的纯理论兴趣，理解空间结构的作用 在塑造模式和克里思的进化动力学是特别及时的，因为通过使用现代 微流体和类器官技术，我们可以开始建立控制拓扑的种群结构， 分子或细胞群体的遗传和迁移模式，放大选择的选择性贝内。 突变，提高了为医疗或工业应用寻找优化蛋白质复合物的能力， 或作为快速复制致病性变体的筛选工具。

Through innovations in both imaging techniques and the ability to process these images at scale, high- resolution imaging is transforming the eld of molecular biology, yet its power has yet to be fully utilized for asking questions in evolutionary biology. Just as demographic surveys can reveal more or less densely populated areas where, for example, a contagious disease may spread at di erent rates, these imaging datasets can help us quantify cellular and molecular patterns of spatial variation and understand how this variation a ects rates of evolution, by impeding or accelerating the spread of new variants through the population. My research program, at the interface of computer vision and evolutionary biology, is exploring how molecular and cellular communities spatially organize, and how the resulting spatial topologies can be generated, stably maintained and further shape the outcome of the evolutionary process. What are spatial topologies that act to amplify the selective advantage of new mutations in the pop- ulation, versus structures that dampen the force of selection and slow down rates of evolution? We build theoretical evolutionary models that explore how the rate of evolution is shaped by complex spatial struc- ture and nd the relevant spatial features for evolutionary ampli cation or selective suppression. We link these theoretical population genetic models to high-resolution imaging datasets and study the resulting spatial architectures. This allows us to go beyond simply describing patterns of cellular or molecular spatial variation, and enables exploration of the generative processes, as well as of the evolutionary trajectories of the system. Beyond the purely theoretical interest in these questions, understanding the role of spatial structure in shaping the mode and tempo of evolutionary dynamics is particularly timely because, by using modern microfuidics and organoid technologies, we can start building population structures that control the topol- ogy and migration patterns of a molecular or cellular population, amplifying the selective bene t of chosen mutations, boosting the ability to nd optimized protein complexes for medical or industrial applications, or as a screening tool for faster replicating pathogenic variants.